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Designing for practicality: a personalized and adaptive framework for real-time EMG-based hand motor decoding.

Parsa Sattari¹, Diba Ravanshid¹, Rezvan Nasiri¹

¹Research Institute for Robotics, Artificial Intelligence, and Information Sciences (RAIIS), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Journal of Neural Engineering

|March 12, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive framework to improve electromyography (EMG) decoding for prosthetic hands by addressing signal variability. The personalized approach significantly enhances decoding accuracy, making prosthetic control more reliable.

Keywords:

electromyography (EMG) signal variability hand motion detection motor decoding framework personalized and adaptive model

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Area of Science:

Biomedical Engineering
Neuroscience
Rehabilitation Technology

Background:

Electromyography (EMG) based decoding for prosthetic hands faces challenges due to signal variability.
Inter-individual, inter-session, and intra-session variabilities significantly impact practical decoder reliability.
Existing decoding methods struggle to maintain accuracy over time and across different conditions.

Purpose of the Study:

To develop and evaluate a novel personalized and adaptive motor decoding framework for robotic prosthetic hands.
To mitigate the impact of EMG signal variabilities on hand motor decoding accuracy.
To improve the practical reliability of EMG-based prosthetic hand control.

Main Methods:

Collected EMG data from 12 participants performing 9 distinct hand motions.
Analyzed EMG signal variabilities using various feature extraction methods and classifier models (MLP, SVM, CNN, KAN).
Evaluated a proposed unsupervised adaptive framework against baseline performance.

Main Results:

Optimal feature extraction window size identified as 100 ms.
EMG signal variabilities, particularly intra-session, were shown to degrade classification accuracy significantly.
The adaptive framework improved accuracy from 80.56% to 88.88%, demonstrating statistically significant enhancement.

Conclusions:

The proposed modular framework effectively addresses EMG signal variabilities for improved prosthetic hand control.
The framework integrates a motion classifier, feature extractor, finite-state machine, and Softmax module for robust decoding.
This adaptive approach offers a significant step towards practical and reliable EMG-based prosthetic hand decoders.